Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants
Abstract
Oranges are one of Indonesia's primary horticultural commodities, with production increasing each year. However, pest and disease infestations often go undetected, leading to significant reductions in crop yields. This study implements Convolutional Neural Network (CNN) technology to identify diseases in orange plants using two architectures: AlexNet and Xception. The implementation results show that the Xception architecture achieved a high accuracy of 96% after 100 training epochs, indicating its effectiveness in disease detection tasks. This research highlights the potential of integrating CNN technology, particularly the Xception model, into web-based systems for disease detection in orange plants. Such systems can assist farmers in maintaining crop health, improving productivity, and ensuring harvest quality.
Downloads
References
I. Nabila, N. Apriani, D. Nur Trisni, and N. Agus Setiawan, “Pengaruh Teknik Pengemasan Pada Komoditas Hortikultura Buah Jeruk,” 2024.
R. H. Ariesdianto, Z. E. Fitri, A. Madjid, and A. M. N. Imron, “Identifikasi Penyakit Daun Jeruk Siam Menggunakan K-Nearest Neighbor,” Jurnal Ilmu Komputer dan Informatika, vol. 1, no. 2, pp. 133–140, Nov. 2021, doi: 10.54082/jiki.14.
D. Irfansyah et al., “Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi,” vol. 6, no. 2, 2021, [Online]. Available: https://data.mendeley.com/datasets/c5yvn32dzg/2.
B. S. Acarya, A. Muhaimin, and K. M. Hindrayani, “Identifikasi Penyakit Daun Jeruk Siam Menggunakan Convolutional Neural Network (CNN) dengan Arsitektur EfficientNet,” G-Tech: Jurnal Teknologi Terapan, vol. 8, no. 2, pp. 1040–1048, Apr. 2024, doi: 10.33379/gtech.v8i2.4120.
I. Awaludin et al., “Analisis Kinerja ResNet-50 dalam Klasifikasi Penyakit pada Daun Kopi Robusta,” Jurnal Informatika, vol. 9, no. 2, 2022, [Online]. Available: http://ejournal.bsi.ac.id/ejurnal/index.php/ji
J. Vicky, F. Ayu, and B. Julianto, “Implementasi Pendeteksi Penyakit pada Daun Alpukat Menggunakan Metode CNN.”
A. Bagas Prakosa and dan Radius Tanone, “Implementasi Model Deep Learning Convolutional Neural Network (CNN) Pada Citra Penyakit Daun Jagung Untuk Klasifikasi Penyakit Tanaman,” 2023. [Online]. Available: https://www.kaggle.com/datasets/n
D. I. Swasono, M. Abuemas, R. Wijaya, and A. Hidayat, “Klasifikasi Penyakit pada Citra Buah Jeruk Menggunakan Convolutional Neural Networks (CNN) dengan Arsitektur Alexnet,” 2023. [Online]. Available: https://www.kaggle.com/datasets/jonathansilva2020/orange-
G. Thiodorus, A. Prasetia, L. A. Ardhani, and N. Yudistira, “Klasifikasi citra makanan/non makanan menggunakan metode Transfer Learning dengan model Residual Network,” Teknologi, vol. 11, no. 2, pp. 74–83, Jul. 2021, doi: 10.26594/teknologi.v11i2.2402.
P. Musa, W. K. Anam, S. B. Musa, W. Aryunani, R. Senjaya, and P. Sularsih, “Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network,” Rekayasa, vol. 16, no. 1, pp. 65–73, Apr. 2023, doi: 10.21107/rekayasa.v16i1.16974.
E. Turnip and A. F. Rozi, “Analisis Perbandingan Arsitektur Convolutional Neural Network pada Klasifikasi Jenis Penyakit Daun Padi,” Jurnal ProTekInfo |, vol. 11, no. 2, 2024.
M. Farij Amrulloh et al., “Klasifikasi Penyakit Daun Bawang Menggunakan Algoritma CNN Xception Penulis Korespondensi,” Online, 2024.
R. A. Putri et al., “Model Deep Learning Untuk Klasifikasi Objek Pada Gambar Fisheye,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 3, pp. 519–528, Jul. 2024, doi: 10.25126/jtiik.938047.
M. Ezar, A. Rivan, D. Alwyn, and G. Riyadi, “53-61 Dokumen diterima pada 08 Februari,” 2021. [Online]. Available: https://jurnal.pcr.ac.id/index.php/jkt/
Y. Brianorman and R. Munir, “Perbandingan Pre-Trained CNN: Klasifikasi Pengenalan Bahasa Isyarat Huruf Hijaiyah,” J. Sistem Info. Bisnis, vol. 13, no. 1, pp. 52–59, Jul. 2023, doi: 10.21456/vol13iss1pp52-59.
R. Kurniawan, P. B. Wintoro, Y. Mulyani, and M. Komarudin, “Implementasi Arsitektur Xception Pada Model Machine Learning Klasifikasi Sampah Anorganik,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 2, Apr. 2023, doi: 10.23960/jitet.v11i2.3034.
Copyright (c) 2024 Venus Al Fatah, Moh. Ali Romli
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
